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Useful Features for Computer-Aided Diagnosis Systems for Melanoma Detection Using Dermoscopic Images
Abstract
The development of performing imaging techniques is favoring the spread of artificial vision systems as support tools for the early diagnosis of skin cancers. Epiluminescence microscopy (ELM) is currently the most adopted technique through which it is possible to obtain very detailed images of skin lesions. Over time, melanoma spreads quickly, invading the body's organs through the blood vessels: an early recognition is essential to ensure decisive intervention. There are many machine learning approaches proposed to implement artificial vision systems operating on datasets made up of dermatoscopic images obtained using ELM technique. These proposals are characterized by the use of various specific features that make understanding difficult: the problem of defining a set of features that can allows good classification performance arises. The aim of this work is to identify reference features that can be used by new researchers as a starting point for new proposals.
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